基于神经网络客观测量MRTD的方法探讨

从分析测试热像仪性能参数MRTD的主观方法入手,提出了一种基于神经网络用面阵CCD测量MRTD的客观方法.介绍客观测量系统的理论框架、设计方案和测试过程,并对其技术难点进行了讨论.     

Experimental design,machine learning approaches for the optimization and modeling of caffeine adsorption

In the current research, the sorption of caffeine on fresh and calcined Cu–Al layered double hydroxide was comparatively studied based on adsorption parameters, adsorption kinetics, and adsorption isotherm. Response surface methodology (RSM), support vector machine (SVM) and artificial neural network (ANN), as data mining methods, were applied to develop models by considering various operating variables. Different characterization methods were exploited to conduct a comprehensive analysis of the characteristics of HDL in order to acquire a thorough understanding of its structural and functional features. The Langmuir model was employed to accurately describe the maximum monolayer adsorption capacity for calcined sample (q_max) of 152.99 mg/g mg/g with R² = 0.9977. The pseudo-second order model precisely described the adsorption phenomenon (R² = 0.999). The thermodynamic analysis also reveals a favorable and spontaneous process. The ANN model predicts adsorption efficiency result with R² = 0.989. The five-fold cross-validation was achieved to evaluate the validity of the SVM. The predication results revealed approximately 99.9% accuracy for test datasets and 99.63% accuracy for experiment data. Moreover, ANOVA analysis employing the central composite design-response surface methodology (CCD-RSM) indicated a good agreement between the quadratic equation predictions and the experimental data, which results in R² of 0.9868 and the highest removal percentages in optimized step were obtained for RSM (pH 5.05, mass of adsorbent 20 mg, time of 72 min, and caffeine concentrations of 22 mg/L). On the whole, the findings confirm that the proposed machine learning models provided reliable and robust computer methods for monitoring and simulating the adsorption of pollutants from aqueous solutions by Cu–Al–LDH.     

盐湖水化学类型的人工神经网络判别方法

研究了作为典型径向基函数网络之一的概率神经网络在盐湖水化学类型分类预测中的应用，验证了该方法的可靠性，得到了满意的分类预测结果。实验结果和网络结构分析表明，概率神经网络方法比熟知的反向传播算法(BP)网络要好。概率神经网络的研究应用为化学模式识别提供了一个新工具。     

Optimisation of the separation of herbicides by linear gradient high performance liquid chromatography utilising artificial neural networks

An artificial neural network (ANN) was employed to model the chromatographic response surface for the linear gradient separation of 10 herbicides that are commonly detected in storm run-off water in agricultural catchments. The herbicides (dicamba, simazine, 2,4-D, MCPA, triclopyr, atrazine, diuron, clomazone, bensulfuron-methyl and metolachlor) were separated using reverse phase high performance liquid chromatography and detected with a photodiode array detector. The ANN was trained using the pH of the mobile phase and the slope of the acetonitrile/water gradient as input variables. A total of nine experiments were required to generate sufficient data to train the ANN to accurately describe the retention times of each of the herbicides within a defined experimental space of mobile phase pH range 3.0-4.8 and linear gradient slope 1-4% acetonitrile/min. The modelled chromatographic response surface was then used to determine the optimum separation within the experimental space. This approach allowed the rapid determination of experimental conditions for baseline resolution of all 10 herbicides. Illustrative examples of determination of these components in Milli-Q water, Sydney mains water and natural water samples spiked at 0.5-1 μg/L are shown. Recoveries were over 70% for solid-phase extraction using Waters Oasis^® HLB 6 cm³ cartridges.     

用于结构信息数值化的电负性拓扑指数方法

本文提出了一种用于结构信息数值化的电负性拓朴指数（ETI）方法．此法是用基团电负性（ETIO）来表达原子及其相邻键的结构信息，这一表达较E－state模型中的本征状态项Ii更为合理．基干ETIO建立的ETI方法可用来在原子水平上直接表达分子的结构信息．应用实例表明，用ETI指数表达结构信息，进而用多元回归及人工神经网络相结合建立结构一活性关系模型适用于某些体系．1结构信息数值化方法中的“直接法”结构信息数值化是应用各种数学及人工智能方法建立QSAR模型的必要前提．其方法大体上可分为两类．一类是通过结构的物化参数来实…     

Simultaneous Spectrophotometric Determination of Iron and Cobalt in Micellar Medium by Using a Principal Component Artificial Neural Network and Multivariate Calibration

A simple and reliable method for simultaneous spectrophotometric determination of iron(II) and cobalt(II) has been established. The method is based on complex formation with 1‐(2‐pyridylazo)‐2‐naphtol (PAN) in a micellar medium. Despite a spectral overlap, Fe²⁺ and Co²⁺ have been simultaneously determined with chemometric approaches involving principal component artificial neural network (PC‐ANN), principal component regression (PCR) and partial least squares (PLS). Various synthetic mixtures of iron and cobalt were assessed and the results obtained by the applications of these chemometric approaches were evaluated and compared. It was found that the PC‐ANN method afforded relatively better precision than that of PCR or PLS. The proposed method permits detection limits of 0.05 and 0.07 ng mL^?1 for Co and Fe, respectively. The influences of pH, ligand amount, solvent percentage and time on the absorbance were also investigated. The proposed method was also applied satisfactorily for the determination of Fe(II) and Co(II) in real and synthetic samples.     

Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis

Two clinical data sets were applied for pattern recognition in order to discover the correlation between urinary nucleoside profiles and tumours. One data set contains 168 clinical urinary samples, of which 84 specimens are from female thyroid cancer patients (malignant tumour group), and the other samples were collected from healthy women (normal group). However, 168 clinical urinary samples comprised the second data set, too. In all the specimens, each number of the samples for both uterine cervical cancer patients (malignant tumour group) and healthy females (normal group) is 60, and the other 48 samples were collected from uterine myoma patients (benign tumour group). For the two data sets, the separation and quantitative determination of the clinical urinary nucleosides were performed by capillary electrophoresis (CE). The pattern recognition was achieved applying multiple layer perceptron artificial neural networks (MLP ANN) based on conjugate gradient descent training algorithm. Moreover, applying the proposed principal component analysis (PCA) input selection scheme to MLP ANN, the accuracy rate of the pattern recognition was improved to some extent (or without any deterioration) even by much simpler structure of MLP ANN. The study showed that MLP ANN based on PCA input selection was a promising tool for pattern recognition.     

Human resource management and performance: A neural network analysis

This study utilizes an innovative research methodology (Kohonen’s Self-Organizing Maps) to explore a subject relatively understudied in Europe. It focuses on the connection between human resource management as a source of competitive advantage and perceived organizational performance in the European Union’s private and public sectors. While practices in these two sectors did not differ significantly, three diverse but overlapping HRM models did emerge, each of which involved a different set of EU member states. Training & Development practices were strongly related to performance in all three models and Communication practices in two. These results show the usefulness of an innovative technique when applied to research so far conducted through traditional methodologies, and brings to the surface questions about the universal applicability of the widely accepted relationship between superior HRM and superior business performance.